Since the coefficients in the regression represent key parameters such as
elasticities and cross-elasticities of demand, it is important that we test their statistical
significance.
The t-test is used for this purpose. For each coefficient, it tests that the
coefficient differs significantly from zero value.
Consider the regression model:
$$ y = b_0+b_1 x_1+b_2 x_2+b_3 x_3 \,…$$
For the coefficient b1, in the above equation:
Null hypothesis H0: b1 = 0
Alternative hypothesis HA: b1 <> 0
Relevant test statistic is t = b1 /σb1, where σb1 is the
standard error of b1.
Relevant distribution is the t-distribution, with degrees of freedom = n − (k + 1).
Where k is the number of explanatory variables, and n is sample size.
Similar tests are run for b2, b3 … to determine the individual
significance of X2 and X3 in the model.
